Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- CodeGoat24/HPD
+- CodeGoat24/OIP
+- CodeGoat24/EvalMuse
+- CodeGoat24/ShareGPTVideo-DPO
+- CodeGoat24/LLaVA-Critic-113k
+- CodeGoat24/VideoDPO
+- CodeGoat24/Text-2-Video-Human-Preferences
+- CodeGoat24/OpenAI-4o_t2i_human_preference
+- CodeGoat24/ImageGen_Reward_Cold_Start
+base_model:
+- CodeGoat24/UnifiedReward-7b
+---
+
+## Model Summary
+
+`Unified-Reward-Think-7b` is the first unified multimodal CoT reward model, capable of multi-dimensional, step-by-step long-chain reasoning for both visual understanding and generation reward tasks.
+
+For further details, please refer to the following resources:
+<!-- - 📰 Paper: https://arxiv.org/pdf/2503.05236 -->
+- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/think
+- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
+- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
+- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
+
+### Quick Start
+All inference codes are provided in our [github](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Think).
+
+We take image understanding assessment as example here:
+~~~python
+# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.conversation import conv_templates, SeparatorStyle
+
+from PIL import Image
+import requests
+import copy
+import torch
+
+import sys
+import warnings
+import os
+
+
+warnings.filterwarnings("ignore")
+pretrained = "CodeGoat24/UnifiedReward-Think-7b"
+model_name = "llava_qwen"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args
+
+model.eval()
+
+url = "https://github.com/LLaVA-VL/blog/blob/main/2024-10-03-llava-critic/static/images/critic_img_seven.png?raw=True"
+image = Image.open(requests.get(url, stream=True).raw)
+image_tensor = process_images([image], image_processor, model.config)
+image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
+
+conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+Query = 'What does this image present?'
+R1 = 'The image is a black and white sketch of a line that appears to be in the shape of a cross. The line is a simple and straightforward representation of the cross shape, with two straight lines intersecting at a point.'
+R2 = 'This is a handwritten number seven.'
+
+question = ("<image>\nGiven a question and a reference image, please analyze in detail the two provided answers (Answer 1 and Answer 2). " \
+            "Evaluate them based on the following three core dimensions:\n" \
+            "1. Semantic accuracy: How well the answer reflects the visual content of the image\n" \
+            "2. Correctness: Whether the answer is logically and factually correct\n" \
+            "3. Clarity: Whether the answer is clearly and fluently expressed\n" \
+            "You may also consider additional dimensions if you find them relevant (e.g., reasoning ability, attention to detail, multimodal grounding, etc.). " \
+            "For each dimension, provide a score from 1 to 10 for both answers, and briefly explain your reasoning. " \
+            "Then, compute the total score for each answer by explicitly adding the scores for all dimensions and showing the full calculation. " \
+            "Enclose your full reasoning within <think> and </think> tags. " \
+            "Then, in the <answer> tag, output exactly one of the following: 'Answer 1 is better' or 'Answer 2 is better'. No other text is allowed in the <answer> section.\n\n" \
+            "Example format:\n" \
+            "<think>\n" \
+            "1. Semantic accuracy: Answer 1 (9/10) - ...; Answer 2 (7/10) - ...\n" \
+            "2. Correctness: Answer 1 (8/10) - ...; Answer 2 (7/10) - ...\n" \
+            "3. Clarity: Answer 1 (9/10) - ...; Answer 2 (8/10) - ...\n" \
+            "[Additional dimensions if any]: Answer 1 (6/10) - ...; Answer 2 (7/10) - ...\n" \
+            "Total score:\nAnswer 1: 9+8+9+6=32\nAnswer 2: 7+7+8+7=29\n" \
+            "</think>\n" \
+            "<answer>Answer 1 is better</answer>\n\n" \
+            "**Note: In the example above, scores and the final answer are placeholders meant only to demonstrate the format. Your actual evaluation should be based on the quality of two given answers.**\n\n"
+            f"Your task is provided as follows:\nQuestion: [{Query}]\nAnswer 1: [{R1}]\nAnswer 2: [{R2}]")
+            
+conv = copy.deepcopy(conv_templates[conv_template])
+conv.append_message(conv.roles[0], question)
+conv.append_message(conv.roles[1], None)
+prompt_question = conv.get_prompt()
+
+input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
+image_sizes = [image.size]
+
+
+cont = model.generate(
+    input_ids,
+    images=image_tensor,
+    image_sizes=image_sizes,
+    do_sample=False,
+    temperature=0,
+    max_new_tokens=4096,
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+print(text_outputs[0])
+~~~
+
+
+## Citation
+
+```
+@article{UnifiedReward,
+  title={Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning.},
+  author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Wang, Chunyu and Lu, Qinglin and Jin, Cheng and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:},
+  year={2025}
+}
+```